
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "99"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 87 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 83 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	0.00%	0.022898	-0.707572	2.492622	-0.921193	0.083814	1.268081	1.460338	-0.827531	0.7452	0.7793	0.2865	2.812991	2.923111
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	0.00%	0.869791	0.668600	3.458911	-0.577146	0.677787	1.006635	0.443037	1.410357	0.6838	0.6920	0.2859	1.568088	1.786628
2459592	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	3.606264	3.970952	41.364391	40.762106	31.257247	26.964215	0.476836	3.422304	0.6671	0.6934	0.4987	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.009585	-0.015040	3.675240	-0.949503	-0.523877	2.064619	-0.465313	0.394075	0.6360	0.6532	0.3990	1.591200	1.558568
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.027725	0.727394	3.638410	-0.603507	3.439210	6.755737	0.469586	0.889514	0.6376	0.6605	0.3822	6.321106	6.032020
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	0.997088	-0.327385	3.016811	-0.027363	3.455788	1.856228	1.650241	-0.237668	0.6706	0.6812	0.3111	1.606411	1.635332
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	-0.276147	-0.571519	3.520782	-1.119821	1.852416	2.069624	1.105013	1.022234	0.7274	0.7238	0.2545	2.836870	2.487607
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	5.642949	0.408731	5.641205	-0.050021	-1.598116	1.166576	-4.675000	4.635800	nan	nan	nan	60.661840	53.639230
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.433338	0.703751	1.885476	2.772105	7.596187	5.121494	2.380271	2.599757	0.6242	0.6496	0.3795	4.192027	3.745501
2459585	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	36.698385	37.127383	16.511854	15.407151	6.099762	3.611157	3.416304	6.194821	0.5970	0.6152	0.3872	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.920273	8.457561	10.354146	1.942187	5.036496	2.178460	45.002033	46.622767	0.6063	0.6216	0.3901	0.877079	0.866507
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	294.598154	297.113279	inf	inf	11582.792213	11457.621117	6090.581087	6072.927806	nan	nan	nan	0.000000	0.000000
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	19.46%	0.00%	0.659920	0.013192	1.981761	1.280922	0.529287	3.101719	-0.320235	1.793036	0.6432	0.6569	0.4066	1.426496	1.428249
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.838428	0.141413	2.810583	1.375162	1.573784	4.763018	0.071048	5.175312	0.6452	0.6599	0.3995	0.000000	0.000000
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.384002	0.429024	3.138821	1.728893	3.184319	3.838957	1.719917	1.863632	0.6351	0.6538	0.4033	6.805378	5.869329
2459578	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	5.704370	4.719422	23.890341	20.436843	28.276676	47.844651	17.460356	36.462477	0.6868	0.7140	0.5098	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.189232	-0.039372	1.944543	1.074469	1.557648	2.178242	-0.665496	0.820144	0.6458	0.6616	0.3990	1.465470	1.420173
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	0.00%	1.810746	-0.368530	1.551462	1.428452	1.094925	1.578103	-0.704323	0.616517	0.6630	0.6837	0.4032	1.483968	1.481810
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.083848	0.665606	1.357376	0.706194	1.162543	1.654587	1.246759	0.809613	0.7227	0.7353	0.3548	1.469526	1.426824
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459573	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	27.57%	0.00%	1.088377	0.153143	1.494906	0.932633	0.593521	1.296799	-0.658271	0.671135	0.6629	0.6796	0.3982	1.387153	1.368006
2459571	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.391444	4.435830	25.670216	21.876084	81.792179	99.763546	23.683584	59.699041	0.6336	0.6765	0.4409	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.623185	0.026710	1.621679	0.980661	1.981544	1.047006	1.026609	1.208740	0.7360	0.7534	0.2505	1.907684	1.695746
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	2.754677	-0.164679	2.205333	1.001635	2.552110	1.600388	0.068911	2.329929	0.6469	0.6636	0.4168	1.375403	1.349356
2459565	digital_ok	0.00%	-	-	-	93.33%	0.00%	1.497506	1.497506	1.384981	1.384981	0.646299	0.646299	0.130033	0.130033	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	6.94%	0.00%	-0.609761	-1.607343	-2.519390	0.330730	0.805122	0.592939	-0.105684	0.824757	0.6702	0.6769	0.4168	1.358503	1.349700
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.626231	0.142335	1.942020	-0.788957	0.647470	4.464373	0.587994	0.629439	0.6653	0.6776	0.4061	4.096514	3.576472
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-1.838516	-1.052561	-1.955085	0.964926	-1.109771	0.033429	-0.151027	1.081025	0.6650	0.6791	0.4065	1.481169	1.438400
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-1.489685	-1.072016	-2.052987	0.525442	-0.773211	-1.056587	-0.240679	0.817155	0.6747	0.6888	0.4015	1.454851	1.408545
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	2.510522	-0.778573	-1.781861	0.648293	-0.056489	-0.788511	0.487409	0.859157	0.6601	0.6730	0.4203	1.470121	1.420657
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.91%	0.00%	3.632652	-1.092138	-1.995648	0.897959	-0.430004	-0.732770	0.309764	0.722210	0.6650	0.6794	0.3964	1.475592	1.420223
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	1.017052	-0.388993	3.121382	0.864005	2.051020	1.905537	0.616492	1.141292	0.6651	0.6783	0.4075	1.468794	1.451017
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.852388	0.115105	3.335484	1.040608	4.059556	1.637709	2.023642	1.978941	0.6623	0.6794	0.3984	4.347819	3.422461
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6764	0.6854	0.4155	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6724	0.6826	0.4133	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-1.538639	-1.119844	-2.570934	0.887727	-1.670277	-0.434919	0.238453	1.100671	0.6690	0.6827	0.4027	1.484015	1.395964
2459550	digital_ok	-	28.01%	28.01%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.4799	0.4870	0.3296	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.852012	-0.292385	3.148369	0.834330	0.742674	3.482537	-0.432600	1.319686	0.6641	0.6790	0.4185	1.359904	1.394794
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	2.966187	0.065180	3.635846	1.734982	1.149408	0.120734	0.911297	1.953054	0.7247	0.7347	0.3667	1.462902	1.370277
2459541	digital_ok	100.00%	1.34%	1.79%	0.00%	100.00%	0.00%	0.438640	-0.405350	4.490977	1.497257	2.510751	1.339065	1.679684	1.505778	0.7160	0.7222	0.3453	5.183275	4.120382
2459540	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.315712	-0.609095	-1.686147	0.267326	-2.567509	4.313318	-0.580263	0.440212	0.7359	0.7324	0.3522	3.576252	2.969996
2459536	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459535	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.198803	-0.603787	-1.770190	0.162858	-1.743437	4.830880	-0.220350	0.317585	0.7729	0.7581	0.3674	3.851508	3.146771
2459534	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	1.318917	0.437150	3.416356	-0.337379	2.494022	3.228680	0.217222	1.407566	0.6975	0.7017	0.4134	1.495055	1.404393
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.118528	1.011050	4.520206	0.585635	2.766849	3.421976	0.341876	2.197842	0.6998	0.7008	0.4077	3.093234	2.605261
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.541222	-0.711609	-1.979009	0.340615	-1.185016	-0.009158	0.200611	0.003120	0.6931	0.7066	0.4253	1.393769	1.360764
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.531345	0.328633	3.794286	-0.347298	7.419683	2.030177	-0.390850	-0.236481	0.9060	0.9356	0.3086	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	0.376456	4.643047	4.794730	0.277339	0.806111	1.642925	-0.891272	3.381100	nan	nan	nan	4.944730	4.183564
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.906723	2.184897	5.111841	0.717389	-0.082739	1.624249	0.390229	0.322928	0.6327	0.7012	0.3839	3.580594	4.870499
2459513	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	5.294203	5.795757	24.743354	22.135945	20.057761	50.502444	11.106667	22.937564	0.8320	0.8700	0.3031	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	1.664946	0.201592	2.537254	-0.952800	3.727499	0.320880	1.817768	1.377105	0.9149	0.9577	0.1667	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.147447	0.332875	4.258276	2.188127	2.086020	3.282237	0.713048	2.714513	0.8705	0.8484	0.2238	7.744719	9.042835
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.118163	-0.226474	4.157974	0.683805	7.305009	3.921782	1.809688	0.345148	0.8699	0.8649	0.4302	0.000000	0.000000
2459501	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.124962	0.196440	4.042635	-0.864751	5.961464	4.097209	-0.322668	-0.513355	0.8470	0.8861	0.3896	0.000000	0.000000
2459500	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.873220	0.349198	3.000749	-0.579794	5.256830	-1.024913	-0.110062	0.557897	0.8519	0.8813	0.3784	0.000000	0.000000
2459499	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.785099	-0.459127	3.592116	-1.133992	4.901077	-0.822876	1.554126	0.143059	0.8910	0.9446	0.1756	0.000000	0.000000
2459498	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.780388	-0.121453	3.415880	-0.903536	4.789175	-0.647010	0.510747	0.790160	0.8610	0.8812	0.4103	0.000000	0.000000
2459497	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.343387	-0.369642	3.863011	-0.770031	6.115652	-0.157565	1.188759	0.200152	0.8951	0.9297	0.3338	0.000000	0.000000
2459496	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.408540	-0.611705	3.283690	-0.653495	4.973324	0.088755	-0.037881	-0.276674	0.0812	0.0638	0.0043	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.238487	-0.637584	2.879943	-0.776059	3.824553	-0.314058	0.437685	-0.043226	0.0832	0.0592	0.0041	0.000000	0.000000
2459494	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.318176	0.231242	3.135090	-0.863863	4.821979	-0.854568	6.519293	0.370738	0.0824	0.0569	0.0035	0.000000	0.000000
2459492	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.607012	0.521214	6.255135	0.961150	7.140664	5.424716	14.175896	2.468722	0.0550	0.0590	0.0045	0.000000	0.000000
2459491	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.610519	-0.408668	5.877790	2.632286	6.392771	4.490349	3.812337	3.929922	0.0719	0.0610	0.0033	0.000000	0.000000
2459490	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.099514	0.766090	5.858523	2.670313	3.973975	3.328782	3.884459	8.557784	0.0788	0.0631	0.0047	0.000000	0.000000
2459489	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.490539	0.363215	5.591009	1.870784	4.741233	6.934939	1.607361	1.296977	0.0762	0.0623	0.0037	0.000000	0.000000
2459488	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.357469	0.613195	5.040500	0.758186	3.730754	4.689940	4.567932	8.163535	0.0641	0.0636	0.0047	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 99 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 99, Node 7:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 99: 2459611

Antenna 99: 2459610

Antenna 99: 2459609

Antenna 99: 2459608

Antenna 99: 2459607

Antenna 99: 2459605

Antenna 99: 2459604

Antenna 99: 2459603

Antenna 99: 2459602

Antenna 99: 2459598

Antenna 99: 2459597

Antenna 99: 2459596

Antenna 99: 2459595

Antenna 99: 2459594

Antenna 99: 2459593

Antenna 99: 2459592

Antenna 99: 2459591

Antenna 99: 2459590

Antenna 99: 2459589

Antenna 99: 2459588

Antenna 99: 2459587

Antenna 99: 2459586

Antenna 99: 2459585

Antenna 99: 2459584

Antenna 99: 2459583

Antenna 99: 2459582

Antenna 99: 2459581

Antenna 99: 2459580

Antenna 99: 2459579

Antenna 99: 2459578

Antenna 99: 2459577

Antenna 99: 2459576

Antenna 99: 2459575

Antenna 99: 2459574

Antenna 99: 2459573

Antenna 99: 2459572

Antenna 99: 2459571

Antenna 99: 2459570

Antenna 99: 2459569

Antenna 99: 2459566

Antenna 99: 2459565

Antenna 99: 2459564

Antenna 99: 2459563

Antenna 99: 2459562

Antenna 99: 2459561

Antenna 99: 2459560

Antenna 99: 2459559

Antenna 99: 2459558

Antenna 99: 2459556

Antenna 99: 2459552

Antenna 99: 2459551

Antenna 99: 2459549

Antenna 99: 2459542

Antenna 99: 2459541

Antenna 99: 2459540

Antenna 99: 2459536

Antenna 99: 2459535

Antenna 99: 2459534

Antenna 99: 2459533

Antenna 99: 2459532

Antenna 99: 2459530

Antenna 99: 2459527

Antenna 99: 2459524

Antenna 99: 2459523

Antenna 99: 2459522

Antenna 99: 2459513

Antenna 99: 2459508

Antenna 99: 2459506

Antenna 99: 2459505

Antenna 99: 2459503

Antenna 99: 2459501

Antenna 99: 2459500

Antenna 99: 2459499

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶